- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed based on multi-factor stock selection models targeting benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices. The goal is to consistently outperform the respective benchmarks [11][12][14] - The construction process of the index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization. The optimization model maximizes single-factor exposure while controlling for constraints such as industry exposure, style exposure, stock weight deviation, turnover rate, and component stock weight ratio [12][41][42] - The Maximized Factor Exposure (MFE) portfolio is used to test the effectiveness of individual factors under real-world constraints. The optimization model is expressed as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where \(f\) represents factor values, \(w\) is the stock weight vector, and constraints include style exposure (\(X\)), industry exposure (\(H\)), stock weight deviation (\(w_b\)), and component stock weight ratio (\(B_b\)) [41][42][43] - The report monitors the performance of common stock selection factors across different sample spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices. Factors are tested using MFE portfolios to evaluate their excess return relative to benchmarks [11][15][18] - The factor library includes over 30 factors categorized into valuation, reversal, growth, profitability, liquidity, volatility, corporate governance, and analyst-related factors. Examples include BP (Book-to-Price), EPTTM (Earnings-to-Price TTM), one-month reversal, three-month reversal, one-year momentum, and others [16][17] - The report highlights the performance of specific factors in different sample spaces: - **CSI 300**: One-month reversal, three-month reversal, and EPTTM one-year percentile performed well recently, while three-month institutional coverage and standardized unexpected earnings performed poorly [1][18] - **CSI 500**: Three-month volatility, three-month reversal, and EPTTM one-year percentile performed well recently, while one-year momentum and standardized unexpected revenue performed poorly [1][20] - **CSI 1000**: One-month volatility, one-month turnover, and three-month reversal performed well recently, while executive compensation and three-month earnings revisions performed poorly [1][22] - **CSI A500**: One-month reversal, EPTTM one-year percentile, and one-month volatility performed well recently, while three-month institutional coverage and one-year momentum performed poorly [1][24] - **Public fund heavy-holding index**: Dividend yield, three-month reversal, and EPTTM performed well recently, while standardized unexpected revenue and three-month earnings revisions performed poorly [1][26][27] - The report tracks the excess returns of public fund index enhancement products, including CSI 300, CSI 500, CSI 1000, and CSI A500. For CSI 300 products, the highest weekly excess return was 0.92%, while the lowest was -3.08%, with a median of 0.01% [3][32][31] - For CSI 500 products, the highest weekly excess return was 3.20%, while the lowest was -0.48%, with a median of 0.49% [3][35][34] - For CSI 1000 products, the highest weekly excess return was 1.58%, while the lowest was -0.82%, with a median of 0.37% [3][37][36] - For CSI A500 products, the highest weekly excess return was 1.20%, while the lowest was -0.84%, with a median of 0.23% [3][40][39]

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Construction Idea**: The MFE portfolio is designed to maximize single-factor exposure while controlling for various real-world constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures the factor's effectiveness under practical constraints [39][40][41] **Construction Process**: The optimization model is formulated as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ - **Objective Function**: Maximize single-factor exposure, where $f$ represents factor values, $f^{T}w$ is the weighted exposure of the portfolio to the factor, and $w$ is the stock weight vector to be solved [39][40] - **Constraints**: - **Style Exposure**: $X$ is the matrix of stock exposures to style factors, $w_b$ is the benchmark weight vector, and $s_l$, $s_h$ are the lower and upper bounds for style factor exposure [40] - **Industry Exposure**: $H$ is the matrix of stock exposures to industries, $h_l$, $h_h$ are the lower and upper bounds for industry exposure [40] - **Stock Weight Deviation**: $w_l$, $w_h$ are the lower and upper bounds for stock weight deviation relative to the benchmark [40] - **Component Weight Control**: $B_b$ is a 0-1 vector indicating whether a stock belongs to the benchmark, $b_l$, $b_h$ are the lower and upper bounds for component weight control [40] - **No Short Selling**: Ensures non-negative weights and limits individual stock weights [40] - **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [41] **Evaluation**: This model effectively tests factor validity under real-world constraints, ensuring the factor's predictive power in practical portfolio construction [39][40][41] Quantitative Factors and Construction Methods - **Factor Name**: Specificity **Construction Idea**: Measures the uniqueness of stock returns by evaluating the residuals from a Fama-French three-factor regression [16][19][23] **Construction Process**: - Formula: $1 - R^2$ from the Fama-French three-factor regression, where $R^2$ represents the goodness-of-fit of the regression model [16] **Evaluation**: Demonstrates strong performance in multiple sample spaces, indicating its effectiveness in capturing unique stock characteristics [19][23][25] - **Factor Name**: EPTTM Year Percentile **Construction Idea**: Represents the percentile rank of trailing twelve-month earnings-to-price ratio (EPTTM) over the past year [16][19][23] **Construction Process**: - Formula: Percentile rank of $EPTTM = \frac{\text{Net Income (TTM)}}{\text{Market Cap}}$ over the past year [16] **Evaluation**: Performs well in various sample spaces, particularly in growth-oriented indices [19][23][25] - **Factor Name**: Three-Month Reversal **Construction Idea**: Captures short-term price reversal by measuring the return over the past 60 trading days [16][19][23] **Construction Process**: - Formula: $\text{Return}_{60\text{days}} = \frac{\text{Price}_{t} - \text{Price}_{t-60}}{\text{Price}_{t-60}}$ [16] **Evaluation**: Effective in identifying short-term reversal opportunities, especially in volatile indices [19][23][25] Factor Backtesting Results - **Specificity Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 1.18% - Monthly Excess Return: 2.02% - Year-to-Date Excess Return: 4.23% - Historical Annualized Return: 0.51% [19] - **Sample Space**: CSI A500 - Weekly Excess Return: 1.43% - Monthly Excess Return: 2.14% - Year-to-Date Excess Return: 2.71% - Historical Annualized Return: 1.72% [25] - **EPTTM Year Percentile Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 0.54% - Monthly Excess Return: 2.01% - Year-to-Date Excess Return: 6.74% - Historical Annualized Return: 3.26% [19] - **Sample Space**: CSI 500 - Weekly Excess Return: 1.01% - Monthly Excess Return: 1.54% - Year-to-Date Excess Return: 1.90% - Historical Annualized Return: 5.24% [21] - **Three-Month Reversal Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 0.49% - Monthly Excess Return: 1.35% - Year-to-Date Excess Return: 4.31% - Historical Annualized Return: 1.13% [19] - **Sample Space**: CSI 1000 - Weekly Excess Return: 1.10% - Monthly Excess Return: 2.15% - Year-to-Date Excess Return: 2.59% - Historical Annualized Return: -0.67% [23] Index Enhancement Portfolio Backtesting Results - **CSI 300 Enhanced Portfolio**: - Weekly Excess Return: 0.78% - Year-to-Date Excess Return: 9.31% [5][14] - **CSI 500 Enhanced Portfolio**: - Weekly Excess Return: -0.52% - Year-to-Date Excess Return: 9.90% [5][14] - **CSI 1000 Enhanced Portfolio**: - Weekly Excess Return: 0.07% - Year-to-Date Excess Return: 15.69% [5][14] - **CSI A500 Enhanced Portfolio**: - Weekly Excess Return: 0.26% - Year-to-Date Excess Return: 9.96% [5][14] Public Fund Index Enhancement Product Performance - **CSI 300 Public Fund Products**: - Weekly Excess Return: Max 1.28%, Min -0.98%, Median 0.12% - Monthly Excess Return: Max 4.10%, Min -0.99%, Median 0.61% - Quarterly Excess Return: Max 5.71%, Min -0.90%, Median 1.52% - Year-to-Date Excess Return: Max 9.84%, Min -0.77%, Median 2.87% [31] - **CSI 500 Public Fund Products**: - Weekly Excess Return: Max 1.41%, Min -1.31%, Median 0.04% - Monthly Excess Return: Max 2.56%, Min -0.60%, Median 0.60% - Quarterly Excess Return: Max 5.51%, Min -0.10%, Median 2.60% - Year-to-Date Excess Return: Max 9.88%, Min -0.77%, Median 4.19% [34] - **CSI 1000 Public Fund Products**: - Weekly Excess Return: Max 0.82%, Min -0.47%, Median 0.15% - Monthly Excess Return: Max 3.55%, Min -0.67%, Median 1.07% - Quarterly Excess Return: Max 7.14%, Min -0.58%, Median 3.21% - Year-to-Date Excess Return: Max 15.34%, Min 0.49%, Median 6.75% [36] - **CSI A500 Public Fund Products**: - Weekly Excess Return: Max 1.16%, Min -0.57%, Median -0.04% - Monthly Excess Return: Max 1.89%, Min -1.55%, Median 0.68% - Quarterly Excess Return: Max 3.76%, Min -1.67%, Median 2.20% [38]

Quantitative Models and Factor Construction Factor Names and Construction - **Factor Name: EPTTM One-Year Percentile** - **Construction Idea**: Measures the percentile rank of the earnings-to-price ratio (EPTTM) over the past year to capture valuation trends[6][17] - **Construction Process**: - Calculate the earnings-to-price ratio (EPTTM) for each stock - Determine the percentile rank of the current EPTTM relative to its distribution over the past year[17] - **Evaluation**: Demonstrated strong performance in certain indices like CSI 1000 and CSI All Share, indicating its effectiveness in capturing valuation signals[6][33][47] - **Factor Name: Pre-Expected PEG** - **Construction Idea**: Combines price-to-earnings ratio with expected growth rates to evaluate valuation adjusted for growth[17] - **Construction Process**: - Calculate the price-to-earnings ratio (PE) - Divide PE by the expected growth rate of earnings to derive the PEG ratio - Use analyst consensus forecasts for expected growth rates[17] - **Evaluation**: Exhibited strong performance in indices like CSI 800 and CSI 500, suggesting its utility in growth-adjusted valuation analysis[6][29][33] - **Factor Name: Six-Month UMR** - **Construction Idea**: Captures momentum adjusted for risk over a six-month window[17] - **Construction Process**: - Calculate the cumulative return over the past six months - Adjust for risk using a volatility or beta-based measure - Normalize the adjusted return to derive the UMR score[17] - **Evaluation**: Consistently effective across multiple indices, including CSI 500 and CSI 1000, highlighting its robustness in momentum strategies[6][25][33] - **Factor Name: Standardized Unexpected Earnings (SUE)** - **Construction Idea**: Measures the deviation of actual earnings from analyst expectations, standardized by the forecast error[17] - **Construction Process**: - Calculate the difference between actual and expected earnings - Standardize this difference using the standard deviation of forecast errors - Derive the SUE score for each stock[17] - **Evaluation**: Effective in identifying earnings surprises, with strong performance in CSI 500 and CSI All Share indices[6][25][47] Factor Backtesting Results - **EPTTM One-Year Percentile** - CSI 1000: Weekly return of 1.09%, monthly return of 0.83%, annualized return of 5.54%[6][33] - CSI All Share: Weekly return of 1.09%, monthly return of -0.34%, annualized return of -4.16%[6][47] - **Pre-Expected PEG** - CSI 800: Weekly return of 0.66%, monthly return of 2.66%, annualized return of 3.11%[6][29] - CSI 500: Weekly return of 0.27%, monthly return of 1.02%, annualized return of -1.79%[6][25] - **Six-Month UMR** - CSI 500: Weekly return of 0.76%, monthly return of 1.14%, annualized return of -3.98%[6][25] - CSI 1000: Weekly return of 0.32%, monthly return of 0.08%, annualized return of 2.58%[6][33] - **Standardized Unexpected Earnings (SUE)** - CSI 500: Weekly return of 0.55%, monthly return of -0.06%, annualized return of 1.46%[6][25] - CSI All Share: Weekly return of 0.32%, monthly return of -0.46%, annualized return of -4.36%[6][47] MFE Portfolio Construction - **Model Description**: Maximized Factor Exposure (MFE) portfolios are constructed to maximize exposure to a single factor while controlling for constraints such as industry and style exposures, stock weight limits, and turnover[61][62] - **Optimization Formula**: $$ \begin{array}{ll} \max & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & 0 \leq w \leq l \\ & 1^{T}w = 1 \\ & \Sigma|w-w_{0}| \leq to_{h} \end{array} $$ - **Explanation**: The objective function maximizes factor exposure, subject to constraints on style, industry, stock weights, and turnover[61][62] - **Evaluation**: Effective in isolating factor performance under realistic portfolio constraints, providing a robust framework for factor validation[61][65]